DOC Ensures that IsotonicRegression passes numpydoc validation (scikit-learn#20514)

Co-authored-by: Guillaume Lemaitre <[email protected]>

Author: embandera

maint_tools/test_docstrings.py

@@ -48,7 +48,6 @@
     "HuberRegressor",
     "IncrementalPCA",
     "Isomap",
-    "IsotonicRegression",
     "IterativeImputer",
     "KBinsDiscretizer",
     "KNNImputer",

sklearn/isotonic.py

@@ -189,6 +189,14 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):
     increasing_ : bool
         Inferred value for ``increasing``.
 
+    See Also
+    --------
+    sklearn.linear_model.LinearRegression : Ordinary least squares Linear
+        Regression.
+    sklearn.ensemble.HistGradientBoostingRegressor : Gradient boosting that
+        is a non-parametric model accepting monotonicity constraints.
+    isotonic_regression : Function to solve the isotonic regression model.
+
     Notes
     -----
     Ties are broken using the secondary method from de Leeuw, 1977.
@@ -348,7 +356,7 @@ def fit(self, X, y, sample_weight=None):
         return self
 
     def transform(self, T):
-        """Transform new data by linear interpolation
+        """Transform new data by linear interpolation.
 
         Parameters
         ----------
@@ -361,7 +369,7 @@ def transform(self, T):
         Returns
         -------
         y_pred : ndarray of shape (n_samples,)
-            The transformed data
+            The transformed data.
         """
 
         if hasattr(self, "X_thresholds_"):